Creating Animated Bubble Charts in D3 - Jim Vallandingham Update: I moved the code to its own github repo - to make it easier to consume and maintain. Update #2 I’ve rewritten this tutorial in straight JavaScript. So if you aren’t that in to CoffeeScript, check the new one out! Recently, the New York Times featured a bubble chart of the proposed budget for 2013 by Shan Carter . As FlowingData commenters point out , the use of bubbles may or may not be the best way to display this dataset. In this post, we attempt to tease out some of the details of how this graphic works. #Simple Animated Bubble Chart In order to better understand the budget visualization, I’ve created a similar bubble chart that displays information about what education-based donations the Gates Foundation has made. You can see the full visualization here And the visualization code is on github **Warning Coffeescript** The example is written in [CoffeeScript]( as I find it much easier to read and write than javascript. #D3’s Force Layout #nodes #gravity #alpha
Vega D3js Voici le premier d’une longue lignée (je l’espère) de tutoriaux en français portant sur la librarie d3.js. Pour en savoir plus sur cette librairie reportez vous à la présentation que j’en ai faite sur ce post. L’objectif de ce premier tutoriel est de faire quelques exemples d’utilisation très simple de la librairie. A l’instart de Jquery, d3.js est une librarie qui permet de manipuler le DOM. Manipuler ou créer une div avec d3.js Ajouter et manipuler un rectangle SVG Dessiner plusieurs éléments dans un groupe svg Gérer des données au format JSON et dessiner en fonction de ces données Dans ce dernier exemple, nous allons construire des nodes qui sont en fait des groupes svg ayant la class “node”. Le dernier exemple introduit plusieurs nouveaux concepts : selectAll(), data(), et enter(). selectAll() data() La methode data() permet de “binder” (attacher) des données à des éléments sélectionnés par la methode selectAll(). enter() Ressources
Predicting future returns of trading algorithms: Bayesian cone | Quantopian Blog Authors: Sepideh Sadeghi and Thomas Wiecki Foreword by Thomas This blog post is the result of a very successful research project by Sepideh Sadeghi, a PhD student at Tufts who did an internship at Quantopian over the summer 2015. Follow her on twitter here. All of the models discussed here-within are available through our newly released library for finance performance and risk analysis called pyfolio. When evaluating trading algorithms we generally have access to backtest results over a couple of years and a limited amount of paper or real money traded data. Here, we will briefly introduce two Bayesian models that can be used for predicting future daily returns. All of these models are available through our newly released library for finance performance and risk analysis called pyfolio. How do we get the model inputs? At Quantopian we have built a world-class backtester that allows everyone with basic Python skills to write a trading algorithm and test it on historical data. Normal model
Data Visualization Libraries Based on D3.JS - Mike McDearmon There are a lot of ways to visualize data on the Web (with more emerging every day), but the flexibility, versatility, and energized development community surrounding D3.js makes it a great option to explore. The following list of D3 plugins, extensions, and applications below is by no means comprehensive, but oughta be enough to keep you busy for a while. If you’re just getting your feet wet with D3.js, here are some great learning resources to get you acclimated:D3 for mere mortals: Great introductory lessons for those starting from scratch.Try D3 Now: Another great resource for learning about core D3 concepts.Data-Driven Documents (paper): An academic article by Mike Bostock with loads of footnotes.Learning D3, Scott Becker: A quick and effective tutorial series to get yourself up and running.Dashing D3: A very thorough tutorial series covering a LOT more than just D3.Interactive Data Visualization for the Web is a fantastic book by Scott Murray.
The Problem with Data Science | Quantopian Blog Data Science is about learning from data, often using Machine Learning and statistics. To do so, we can build statistical models that provide answers to our questions or make predictions based on data we have collected. Ideally, we build the model that most accurately describes our data, makes the best predictions, and provides the answers of interest. Once we have our dream model we just have to figure out how to fit it to data (i.e. do inference). Graphically, this is how I think the process should look like: Unfortunately, as anyone who has done such a thing can attest, it can be extremely difficult to fit your dream model and requires you to take many short-cuts for mathematical convenience. So a lot of times we don't build the models we think best capture our data but rather the models we can make inference on. Think about that for a second, you're not tied to pre-specified statistical model like a frequentist T-Test that some statistician worked out how to do inference on.
Data Visualization 101: Pie Charts In our Data Visualization 101 series, we cover each chart type to help you sharpen your data visualization skills. Pie charts are one of the oldest and most popular ways to visualize data. This classic chart is the perfect example of the power of data visualization: a simple, easy-to-understand presentation that helps readers instantly identify the parts of a whole. What It Is The typical pie chart is divided into sections that illustrate a numerical proportion. Where It Came From Scottish engineer William Playfair is generally credited with creating the world’s first pie charts back in 1801. Playfair’s pie chart showed the proportion of land held by the Turkish Empire in Asia, Europe and Africa. Although he invented the form, Playfair never called his invention a “pie chart.” Another series of Playfair’s pie charts in “Chart Representing the Extent, Population & Revenue of the Principal Nations in Europe in 1804.” After Playfair’s initial work, the pie chart began to pick up steam. 1. 2.
» Build a web app fast: Python, HTML & JavaScript resources Wanna build a web app fast? Know a little bit about programming but want to build a modern web app using two well-supported, well-documented, and universally accessible languages? You’ll love these Python, HTML/CSS, and JavaScript resources. I’ve been sharing these documents with friends who ask me, “I want to start programming and build a web app, where do I start?”. These resources have also been useful to existing programmers who know C, C++ or Java, but who want to embrace dynamic and web-based programming. Python Resources Python is the core programming language used at Parse.ly. I’ve written a blog post with some original materials for learning Python, import this — learning the Zen of Python with code and slides. This is a good starting point, but you may also find these resources very helpful: For absolute beginners, “Learn Python the Hard Way”. HTML/CSS Resources In order to build up web applications, you’ll need to write your front-ends in HTML and CSS. JavaScript Resources Django
Comptes des ménages - Revenu disponible des ménages Le revenu net réel disponible des ménages est défini comme la somme de leurs dépenses de consommation finale et de leur épargne, diminuée de la variation de leurs droits nets sur les fonds de pension. Cet indicateur correspond également à la somme des salaires et traitements, du revenu mixte, des revenus nets de la propriété, des transferts courants nets et des prestations sociales autres que les transferts sociaux en nature, moins les impôts sur le revenu et le patrimoine et les cotisations de sécurité sociale payées par les salariés, les travailleurs indépendants et les chômeurs. Le revenu disponible ajusté brut des ménages y ajoute les « revenus » des administrations publiques et des institutions sans but lucratif au service des ménages (ISBLSM) pour refléter les transferts sociaux en nature.